The retinoic acid receptor-related orphan receptor gamma, or RORγ, is a member of the nuclear receptor ROR family and is involved in important physiological processes including development, reproduction and metabolism. Its activity is regulated by the binding of small compounds, called ligands, which can induce conformational changes, which in turn, can influence the binding of cofactors, including transcriptional coactivators and corepressors,. There are two isoforms of RORγ. The first is RORγ, which is expressed in the liver, adipose tissue, skeletal muscle and kidneys, and is involved in glucose and lipid metabolism and regulating the circadian rhythm. The second is RORγt, which is expressed in the immune system, and is involved in thymopoiesis and secondary lymphoid organ development. It has been shown that RORγ is involved in the progression of various diseases, including metabolic and autoimmune diseases. These diseases can be controlled through natural or synthetic ligands that can either agonize or antagonize RORγ.
Previous in vitro screens have identified interacting ligands of RORγ, but many of these fail in humans due to metabolic inactivation, failure to reach target tissues and off-target toxicity; therefore, it will be much more effective to develop an in vivo screening system that can ensure that the ligand is interacting with the receptor in the correct in vivo setting. Our lab has developed an in vivo GFP reporter system, incorporated into the zebrafish genome, that allows high throughput screening to identify ligands that target RORγ. A drug screen using a compound library consisting of ~4000 natural compounds and approved drugs is being performed to identify new interacting ligands of RORγ. Using new identified ligands of RORγ, cofactors and target genes that are associated with RORγ can also be discovered, which will allow for further understanding of the role and function of RORγ as well as its involvement in diseases. Ultimately, the active drugs identified during the drug screen can be tested on various disease models.